Yu-Ling Lu

Physical and virtual labs have unique capabilities that can influence how students learn from them. The purpose of this study was to examine the effect of physical and virtual manipulatives on students’ learning of heat and temperature and to examine the influence of various combinations of physical and virtual manipulatives. A total of 205 participants were divided into four groups: only physical manipulatives, only virtual manipulatives, physical-virtual manipulatives, and virtual-physical manipulatives. Students’ knowledge acquisition was tested using Pre-test–Post-test design. The results showed that physical and virtual manipulatives are as effective in facilitating students’ learning of state changes, but virtual manipulatives are more beneficial to students’ learning of thermal expansion and contraction than physical manipulatives are. Physical-virtual manipulatives are more effective than virtual-physical manipulatives or physical manipulatives alone are to promote students’ learning of heat and temperature, but this effect is similar to that of virtual manipulatives alone. The results suggest that virtual laboratories can effectively model abstract concepts. The better effect of the physical-virtual operation sequence on learning does not depend on whether the two types of experiments are combined or on the sequence of operations but on the type of experiment when its advantages actually contribute to learning. Keywords: experimental design, heat and temperature, physical manipulative, virtual manipulative

This study gathers and examines information about the flow state’s emergence during tests and its factors using an electroencephalogram (EEG) to establish a method and reveal an individual student’s flow construct. Through a single-case experimental design and 766 test items, multiple measurements were performed on a 14-year-old junior high school science-gifted student. During the test, self-efficacy, item difficulty, cognitive load, and test performance (long-term test performance [LT-tp] and short-term test performance [ST-tp]) were examined to establish the construct of EEG-detected, real-time flow states (EEG-Fs). Based on the chi-square test of independence results, the EEG-F had a significant correlation with the student’s cognitive load, self-efficacy, LT-tp, and item difficulty. Furthermore, a J48 decision tree analysis and logistic regression revealed four inhibiting and two inducing conditions affecting the emergence of EEG-Fs. The two inducing conditions included (1) high self-efficacy with a low cognitive load (odds ratio (OR) = 3.7) and (2) high cognitive load when combined with high self-efficacy and LT-tp for low-difficulty items (OR = 3.5). The established method and findings may help teaching designers or automated teaching applications detect the individual student’s flow construct to select appropriate test tasks accordingly, resulting in an optimal experience and better achievements.

Although game-based interactive technology has long enhanced flow experiences crucial for learning, its effects have been unclear. Thus, this study gathered students’ electroencephalogram (EEG) information during their work in game-based learning environments with different levels of technological interactivity (LTIs; low, mid, and high LTIs). Multiple measurements were used in a relatively small sample, and 3 9th graders (age 15) of different learning environments worked on 360 test items. The EEG data were analyzed with the LTI, balance of challenge and skill (BCS), and sense of control (SC) to establish the flow state construct. A chi-square test showed a significant association between flow states and the LTI, whereas a J48 decision tree analysis and logistic regression demonstrated that inflow experiences would likely emerge in students with high short-term SC (ST-SC), high BCS, and high-LTI learning environments. Furthermore, in high ST-SC and high BCS cases, the odds ratio (OR) of emerging inflow experiences with a high LTI is eight times more than the rest, suggesting that instructional designers (and teachers) use high-LTI game-based learning environments while ensuring students’ learning with adequate SC and BCS.

Previous studies measured flow states using students’ self-reported experiences, resulting in issues regarding nonobjective and nonreal-time data. Thus, this study used an electroencephalogram (EEG) to measure the EEG-detected real-time flow states (EEG-Fs) of 30 students from the 4th and 5th grades. Their EEG measurements, self-reported reflective flow experiences (SR-Fs), grade levels (GLs), balance of challenge and skill (BCS), and sense of control, represented by their overall test performance (OA-tp) and momentary test performance (MOM-tp), were analyzed to establish their EEG-F’s construct. Based on the results of a chi-square test, the EEG-F correlates significantly with SR-F, BCS, OA-tp, and MOM-tp. A J48 decision tree analysis and logistic regression further revealed that in-flow experiences (in-EEG-F) were detected when students had high SR-Fs, where the BCS contributed to flow states. In particular, students with a low-challenge/high-skill BCS demonstrated an in-EEG-F state upon having a high OA-tp. For high-challenge/high-skill, the in-EEG-F state was determined through their MOM-tp. Through the EEG and flow state construct, this study revealed a whole-part association between students’ momentary and overall reflective flow experiences and identified viable paths for inducing students’ EEG-Fs, which can contribute to future e-learning development when integrated with a brain-computer interface for e-learning or e-evaluation systems.

Predicting students' line of actions helps educators give adequate guidance to students, but this remains a challenge in science, technology, engineering, and mathematics (STEM) education. Given this, there is a scarcity of related research that will help improve teachers' prediction capabilities on students' line of actions when tackling ill-defined problems (IDPs), as well as how emerging data mining techniques could contribute to such prediction. The present study aims to fill the gap by measuring the quality of teachers' predictions (labeled expert prediction), where 43 elementary teachers predict students' step-by-step actions when solving an IDP through the light path task (LPT), and then comparing its quality with that of machine prediction, executed via sequential pattern mining techniques. Data on students' lines of action were collected from 501 5th- and 6th-grade students, aged 11-12. The results showed the significantly lower accuracy of expert prediction compared to machine prediction, which highlights the advantages of using data mining in predicting students' actions and shows its possible application as a recommendation system to provide adaptive guidance in future STEM education.

Studies have shown that although traditional instruction is effective in developing students’ conceptual understanding, it lacks in strengthening their thinking skills. To enhance traditional instruction and establish an approach using game-based instruction, this study developed Multi-genre digital Game-based Instruction (MGI), which integrates various game genres—situated and competitive—with traditional instruction. A total of 115 fourth-graders (9–10 years old) of 4 classes participated in the study, of which 2 classes made up the MGI (experimental) group and the other 2, the traditional instruction (control) group. Quantitative and qualitative data were collected to answer research questions by examining the comparative effectiveness of the two groups separately as well as the effectiveness by achievement level, and exploring students’ learning experiences. Findings showed that using MGI resulted in a significant improvement in conceptual understanding and argumentation skills compared to those who were taught only with traditional instruction. Furthermore, it showed that MGI was significantly more effective than traditional instruction in improving low-achieving students’ conceptual understanding, as well as improving argumentation skills for all achievement levels. Students’ learning experiences with MGI showed how the proposed instruction is superior compared to existing traditional instruction.

For the purpose of introducing the trends and development of science teacher education in Taiwan, three stages of teacher education were explored in this study: the distant past (DP): 1949 to 1994; the immediate past (IP):1994 to 2017; and the present and future (PF): 2018 and beyond. Each of the three stages has had a different emphasis on science teacher education. In the DP stage, science teacher education in Taiwan focused mainly on preparing elementary and secondary school science teachers with sufficient science knowledge, pedagogical content knowledge and to be good role models in the classroom. During the IP stage, the “Teacher Education Act” [TEA] was enacted in 1994, and the teacher education system in Taiwan changed from a centralized system to a decentralized one. Multiple channels were opened for teacher preparation. In this period of time, the purpose of science teacher education shifted from providing good role models and sufficient science knowledge to developing competency-based professional science teachers. All teacher education programs in Taiwan could offer science teacher education training courses for pre-service science teachers. In addition, the teaching internship duration was reduced from 1 year to one semester. Teacher certification became a dominant focus in each county or school. During the PF stage, an amended TEA was implemented in 2017, and 12-year continued curriculum standards were officially implemented in the public school system in 2019. The 12-year continued curriculum standards address inquiry, practices and cross-discipline instruction. Science teachers need not only to teach science content knowledge, but also to integrate content knowledge, inquiry competence and other discipline areas into their teaching. The demand for good science teachers is increasing. Meanwhile, the low recruitment rate for science teachers impedes the public’s willingness to enroll in teacher education programs. Few teachers with bachelor degrees can pass the teacher certification exam necessary to be hired in teaching positions. Although the conditions for pre-service science teachers are harsh, there is demand in our teacher education system to recruit science teachers with talent and passion.

Students’ engagement in a learning environment has been a crucial factor in evaluating the quality of learning and predicting their academic success. With technologies being more applied in science education for enhancing learning engagement, it is important to study how learning environments with different levels of technological engagement (LTEs) affect students’ learning. Data were collected from 168 fourth-grade students, ages 9–10, divided into 3 groups of different learning environments: (1) game-based instruction (high LTE) and (2) video-based instruction (medium LTE) as the two experimental groups, and (3) traditional instruction (low LTE) as the control group. Students’ learning was monitored with two tests: scientific concept test and the scientific argumentation skills test. Descriptive and inferential statistics including t-test, ANCOVA, and the Johnson-Neyman procedure were used to examine how different LTEs affect students’ learning as a group and by level (high-, medium-, and low-achieving). Findings demonstrated that (a) three forms of technology-supported learning significantly improve students’ scientific knowledge and argumentation skills; (b) higher LTEs lead to significantly better learning of scientific knowledge, except for some high-achieving students who can learn equally well in traditional instruction; and (c) higher LTEs lead to significantly better learning of argumentation skills across all students.